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Our mission on the Advertising Product & Technology team is to build a next generation advertising platform that aligns with our unique value proposition for audio and video. We work to scale the user experience for our fans and hundreds of thousands of advertisers. This scale brings unique challenges as well as tremendous opportunities for our artists and creators.
We are currently recruiting for a Data Scientist within the multidisciplinary Advertising Product Insights team. This role is focused on supporting the scaling of the future of Spotify Advertising, our self-service advertising platform, Spotify Ads Manager. Our work sits at the intersection of R&D and the Ads business, and we are responsible for bringing the right data and insights to our breadth of stakeholders to understand Spotify Ads Manager performance, and the advertiser experience with Spotify.
Our mission is to enable the product and business teams to meet their objectives through evidence based decision making and customer focus. As a data scientist in this group you will use a range of data science tools and capabilities to work closely with product, design, engineering, user research, product marketing, and our business stakeholders on one or more of the following areas:
Customer facing product development for advertisers
Data requirements and management for our ever expanding platform
Spotify Advertiser and Spotify Ads Manager growth initiatives
Experimentation, causal inference and Insights for Spotify Ads Manager optimization
Defining new metrics, forecasts, and benchmarks for product evolution
Evolving Ads Manager self-service performance and UX reporting
What You'll Do
Who You Are
Where You'll Be
Additional Information
The United States base range for this position is $107,766 - $153,951, plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays. This range encompasses multiple levels. Leveling is determined during the interview process. Placement in a level depends on relevant work history and interview performance. These ranges may be modified in the future.
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